class_docstr: Class for Hugging Face sequence classification models trained on a NLI dataset
  
  Attributes:
      model_path: path to HF model
      tokenizer_path: path to HF tokenizer
      name: string identifier for the model. Lowercase letters and numbers.
          No spaces/special characters except dashes.
      description: String description of the model.
      tasks: List of supported task strings
      details: Dictionary of additional details about the model
      model_class: Class used to initialise model
      tokenizer_class: Class used to initialise tokenizer
      device: Device for model. Defaults to "cuda" if available.
code: def __init__(
      self,
      model_path=None,
      tokenizer_path=None,
      name: str = None,
      description: str = None,
      tasks: List[str] = None,
      details: Dict = None,
      model_class=AutoModelForSequenceClassification,
      tokenizer_class=AutoTokenizer,
      device=None,
  ):
      tasks = tasks or ["text-classification"]
  
      HFModel.__init__(
          self,
          model_path,
          name=name,
          description=description,
          tasks=tasks,
          details=details,
          tokenizer_path=tokenizer_path,
          model_class=model_class,
          tokenizer_class=tokenizer_class,
          device=device,
      )
  
file_path: backprop/backprop/models/hf_nli_model/model.py
imports: from typing import List, Dict
  from transformers import AutoModelForSequenceClassification, AutoTokenizer
  from backprop.models import HFModel
  import torch
label_desc: Use image node_id 1 to create a new HFNLIModel object from inherited base classes: HFModel with example: obj = HFNLIModel(model_path, tokenizer_path, name, description, tasks, details, model_class, tokenizer_class, device)